ArticleBJA open2026
Digital health technologies and artificial intelligence for shared decision making in acute pain management: recent advances and perspectives.
Article in BJA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute pain management is shifting away from traditional paternalistic models towards more collaborative, patient-centred approaches that emphasise shared decision making and the use of digital health technologies. Here, we review recent advances in digital health technologies and artificial intelligence (AI) that support shared decision making, including interactive decision aids, video narratives, and AI-assisted tools. These innovations have the potential to optimise analgesia, reduce the risks associated with opioids and opioid-related harms, and enhance patient autonomy across surgical, emergency, and primary care settings. Although emerging evidence shows promise in improving prescribing efficiency and increasing patient satisfaction, there are still significant barriers to implementation. These include resource limitations, challenges in selecting appropriate tools, and the necessity for robust validation of AI systems. Herein, we critically evaluate the current evidence, discuss practical strategies for implementation, and provide recommendations for integrating these technologies into routine clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.